Data & IA engineer (F/M) Ajouter aux favoris
- Designing and deploying efficient, secure, and high-performing data pipelines within Microsoft Fabric that seamlessly integrate data from multiple enterprise sources.
- Delivering production-grade, well-structured lakehouses or warehouses optimized for analytics, machine learning, and real-time AI workloads.
- Automating ingestion, transformation, and orchestration processes using Fabric Data Pipelines, Dataflows Gen2, and supporting Azure services, ensuring low latency and high reliability.
- Implementing monitoring, testing, and CI/CD practices for data assets and AI models to ensure consistency, scalability, and compliance with data governance frameworks.
- Proactively improving performance, reliability, and cost efficiency across Fabric workloads.
- Implement data ingestion using Fabric Data Pipelines, Dataflows Gen2 , and event-driven sources.
- Develop scalable transformations using Spark (Fabric notebooks) and SQL (Warehouse / Lakehouse) .
- Model data for analytics and AI using medallion architecture (Bronze / Silver / Gold).
- Ensure data quality, lineage, and observability using Fabric monitoring and Purview integration.
- Document data pipelines, schemas, and reusable pattern.
- Build real-time data pipelines using Fabric Real-Time Intelligence and other Integration technologies .
- Integrate event sources.
- Operate and maintain streaming architectures with failure handling.
- Design and deploy RAG (Retrieval-Augmented Generation) architectures and agentic workflows using Fabric data and AI Foundry.
- Build and operate automated inference pipelines (batch and real-time) integrated directly with the Fabric Lakehouse .
- Transform AI prototypes into enterprise-grade APIs and services, ensuring scalability and low-latency performance.
- Set up tracking for model performance, token usage, and AI safety (guardrails, drift, and output quality).
- Support data scientists by operationalizing notebooks, feature engineering pipelines, and ML models within Fabric.
- Ensure compliance with responsible AI standards and auditing requirements.
- Enable semantic models and datasets for Power BI .
- Optimize models for performance, refresh reliability, and cost.
- Support self-service analytics while enforcing governance standards.
- Implement CI/CD for Fabric (GitHub integration, deployment pipelines).
- Apply data governance and security (Purview, RBAC, sensitivity labels).
- Optimize capacity usage and cost (Fabric capacity management).
- Ensure compliance with enterprise data and AI standards.
- Monitor pipeline health, data freshness, and usage metrics through Fabric and Azure monitoring tools.
- Participate in incident response and platform optimization initiatives.
- Work closely with data scientists, BI developers, architects, and product teams.
- Translate business requirements into scalable Fabric solutions.
- Contribute to platform standards, reusable assets, and documentation.
- Provide technical guidance to less experienced engineers and promote best practices.
- Core Skills
- Strong Python and SQL .
- Hands-on experience with Microsoft Fabric :
- Lakehouse
- Data Pipelines / Dataflows Gen2
- Fabric Spark
- Fabric Warehouse
- Solid understanding of data modeling and analytical design patterns.
- Experience with medallion architecture and performance-optimized transformations.
- Microsoft Ecosystem
- Azure services: Azure Data Factory, Event Hubs, Azure ML, Azure OpenAI .
- Power BI semantic modeling (Direct Lake, import, incremental refresh).
- Identity and security using Azure Entra ID .
- Familiarity with Purview lineage, classifications, and access governance.
- Software Engineering Mindset
- GitHub-based development and CI/CD.
- Monitoring, logging, and operational support.
- Cost-aware design (capacity sizing, workload isolation).
- Experience with unit testing for data transformations, validation frameworks, and deployment automation.
- Preferred Qualifications
- Experience with Fabric Real-Time Intelligence (KQL) .
- Experience with LLM-based architectures (RAG, vector search, copilots).
- Familiarity with Microsoft Purview and enterprise data governance.
- Experience operating large-scale Fabric capacities across domains.
- Fluency in English is required; proficiency in other languages spoken in the EMEA region is a plus.
- Pre-screening interview
- Interview with Manager and technical interview
- HR Interview
- Referral calls
- Interview with SVP Data, AI and Analytics
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